Contributions to the theory of stochastic programming

Two stochastic programming decision models are presented. In the first one, we use probabilistic constraints, and constraints involving conditional expectations further incorporate penalties into the objective. The probabilistic constraint prescribes a lower bound for the probability of simultaneous occurrence of events, the number of which can be infinite in which case stochastic processes are involved. The second one is a variant of the model: two-stage programming under uncertainty, where we require the solvability of the second stage problem only with a prescribed (high) probability. The theory presented in this paper is based to a large extent on recent results of the author concerning logarithmic concave measures.This work was supported in part by the Institute of Economic Planning, Budapest.This paper was presented at the 7th Mathematical Programming Symposium 1970, The Hague, The Netherlands, under the title Programming under probabilistic constraints and programming under constraints involving conditional expectations.